Search for: All records

Abstract This work explores the impacts of magnetogram projection effects on machine-learning-based solar flare forecasting models. Utilizing a methodology proposed by D. A. Falconer et al., we correct for projection effects present in Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set. We then train and test a support vector machine classifier on the corrected and uncorrected data, comparing differences in performance. Additionally, we provide insight into several other methodologies that mitigate projection effects, such as stacking ensemble classifiers and active region location-informed models. Our analysis shows that data corrections slightly increase both the true-positive (correctly predicted flaring samples) and false-positive (nonflaring samples predicted as flaring) prediction rates, averaging a few percent. Similarly, changes in performance metrics are minimal for the stacking ensemble and location-based model. This suggests that a more complicated correction methodology may be needed to see improvements. It may also indicate inherent limitations when using magnetogram data for flare forecasting. 

Although analogical examples can support better understanding of new science concepts, when analogies are included superficially and without explanation they may have unintended negative effects. In this study, simple inclusion of analogies greatly increased the perceived familiarity and predicted understanding of geology concepts. Higher judgements of familiarity and predicted understanding could produce illusions of understanding when reading texts about these concepts. 

Although analogical examples can support better understanding of new science concepts, when analogies are included superficially and without explanation they may have unintended negative effects. In this study, simple inclusion of analogies greatly increased the perceived familiarity and predicted understanding of geology concepts. Higher judgements of familiarity and predicted understanding could produce illusions of understanding when reading texts about these concepts. 

Abstract This study explores the behavior of machine-learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set, we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, and multilayer perceptron performance. We implement our classifiers using three temporal training windows: stationary, rolling, and expanding. The stationary window trains models using a single set of data available before the first forecasting instance, which remains constant throughout the solar cycle. The rolling window trains models using data from a constant time interval before the forecasting instance, which moves with the solar cycle. Finally, the expanding window trains models using all available data before the forecasting instance. For each window, a number of input features (1, 5, 10, 25, 50, and 120) and temporal sizes (5, 8, 11, 14, 17, and 20 months) were tested. To our surprise, we found that, for a window of 20 months, skill scores were comparable regardless of the window type, feature count, and classifier selected. Furthermore, reducing the size of this window only marginally decreased stationary and rolling window performance. This implies that, given enough data, a stationary window can be chosen over other window types, eliminating the need for model retraining. Finally, a moderately strong positive correlation was found to exist between a model’s false-positive rate and the solar X-ray background flux. This suggests that the solar cycle phase has a considerable influence on forecasting. 

Abstract Meteoroids of sub‐milligram sizes burn up high in the Earth's atmosphere and cause streaks of plasma trails detectable by meteor radars. The altitude at which these trails, or meteors, form depends on a number of factors including atmospheric density and the astronomical source populations from which these meteoroids originate. A previous study has shown that the altitude of these meteors is affected by long‐term linear trends and the 11‐year solar cycle related to changes in our atmosphere. In this work, we examine how shorter diurnal and seasonal variations in the altitude distribution of meteors are dependent on the geographical location at which the measurements are performed. We use meteoroid altitude data from 18 independent meteor radar stations at a broad range of latitudes and investigate whether there are local time (LT) and seasonal variations in the altitude of the peak meteor height, defined as the majority detection altitude of all meteors within a certain period, which differ from those expected purely from the variation in the visibility of their astronomical source. We find a consistent LT and seasonal response for the Northern Hemisphere locations regardless of latitude. However, the Southern Hemisphere locations exhibit much greater LT and seasonal variation. In particular, we find a complex response in the four stations located within the Southern Andes region, which indicates that the strong dynamical atmospheric activity, such as the gravity waves prevalent here, disrupts, and masks the seasonality and dependence on the astronomical sources. 

Abstract An unusual sudden stratospheric warming (SSW) event occurred in the Southern Hemisphere in September 2019. Ground‐based and satellite observations show the presence of transient eastward‐ and westward‐propagating quasi‐10 day planetary waves (Q10DWs) during the SSW. The planetary wave activity maximizes in the mesosphere and lower thermosphere region approximately 10 days after the SSW onset. Analysis indicates that the westward‐propagating Q10DW with zonal wave numbers = 1 is mainly symmetric about the equator, which is contrary to theory which predicts the presence of an antisymmetric normal mode for such planetary wave. Observations from microwave limb sounder and sounding of the atmosphere using broadband emission radiometry are combined with meteor radar wind measurements from Antarctica, providing a comprehensive view of Q10DW wave activity in the Southern Hemisphere during this SSW. Analysis suggests that the Q10DWs of various wavenumbers are potentially excited from nonlinear wave‐wave interactions that also involve stationary planetary waves withs = 1 ands = 2. The Q10DWs are also found to couple the ionosphere with the neutral atmosphere. The timing of the quasi‐10‐day oscillations (Q10DOs) in the ionosphere are contemporaneous with the Q10DWs in the neutral atmosphere during a period of relatively low solar and geomagnetic activity, suggesting that the Q10DWs play a key role in driving the ionospheric Q10DOs during the Southern SSW event. This study provides observational evidence for coupling between the neutral atmosphere and ionosphere through the upward propagation of global scale planetary waves.